Context

#

Learned in this study

#

Things to explore

#

Overview

#

Notes

#

3 Predictron Architecture

#

• The predictron is composed of four main components
  • A state representation $\textbf{s} = f(s)$ that encodes raw input $s$
  • A model $\textbf{s}’$, $\textbf{r}$, $\boldsymbol{\gamma} = m(\textbf{s}, \beta)$ that maps from internal state $\textbf{s}$ to subsequent internal state $\textbf{s}’$, internal reward $\textbf{r}$, and internal discount $\boldsymbol{\gamma}$
  • A value function $v$ that outputs internal values $\textbf{v} = v(\textbf{s})$ representing the future, internal return from internal state $\textbf{s}$ onwards
  • An accumulator, which combines together internal rewards, discounts, and values, into an overall estimate of value $\textbf{g}$

See also

#

References

#

• Silver, David, et al. “The predictron: End-to-end learning and planning.” arXiv preprint arXiv:1612.08810 (2016).